Copy Is All You Need (2307.06962v1)

Abstract: The dominant text generation models compose the output by sequentially selecting words from a fixed vocabulary. In this paper, we formulate text generation as progressively copying text segments (e.g., words or phrases) from an existing text collection. We compute the contextualized representations of meaningful text segments and index them using efficient vector search toolkits. The task of text generation is then decomposed into a series of copy-and-paste operations: at each time step, we seek suitable text spans from the text collection rather than selecting from a standalone vocabulary. Experiments on the standard LLMing benchmark (WikiText-103) show that our approach achieves better generation quality according to both automatic and human evaluations. Besides, its inference efficiency is comparable to token-level autoregressive models thanks to the reduction of decoding steps. We also show that our approach allows for effective domain adaptation by simply switching to domain-specific text collection without extra training. Finally, we observe that our approach attains additional performance gains by simply scaling up to larger text collections, again without further training.\footnote{Our source codes are publicly available at \url{https://github.com/gmftbyGMFTBY/Copyisallyouneed}.}

• The paper proposes the CoG framework, which replaces token-level prediction with efficient phrase-level copying from a pre-encoded corpus.
• The methodology employs a prefix encoder and phrase encoder to generate contextualized phrase vectors, achieving improved generation quality on benchmarks like WikiText-103.
• The approach enables plug-and-play domain adaptation with faster inference, demonstrating scalability and high-quality performance across diverse corpora.

Copy is All You Need: A Reformulation of Text Generation

The paper "Copy is All You Need" explores a novel approach to text generation, deviating from the traditional methods employed by autoregressive neural LLMs. Rather than producing output by sequentially predicting and selecting the next token from a predefined vocabulary, this research proposes a paradigm where text generation is conducted through progressively copying text segments from a pre-existing corpus. The key methodology introduced in the paper, termed CoG (Copy-Generator), leverages the contextualized representations of text segments, enabling efficient selection and assembly of phrases to manifest text continuations.

Summary

The CoG framework consists of three primary components: a prefix encoder, a phrase encoder, and a set of context-independent token embeddings. The prefix encoder, akin to conventional models, generates representations for initial input sequences. Meanwhile, the phrase encoder uniquely computes contextualized vector representations for text segments within a given corpus, which are then stored in an offline index for accessibility during text generation. This method allows the retrieval of text sequences as potential next-token predictions, circumventing the need for iterative token-level prediction.

The authors conducted comprehensive experiments using the WikiText-103 benchmark to evaluate CoG's effectiveness. The results delineated that the CoG model surpasses traditional neural LLMs in terms of generation quality, as evidenced by both automated metrics and human evaluation. The paper reports a notable advantage in terms of inference efficiency, attributing this to the reduction in the number of decoding steps facilitated by phrase-level generation. Moreover, the adaptability of CoG was tested by switching the source text collection to a domain-specific corpus (Law-MT), and also by scaling the corpus size using the En-Wiki dataset. CoG demonstrated superior performance in these varied settings without necessitating additional training, underscoring its potential for plug-and-play domain adaptation.

Implications of the Research

The theoretical implications of CoG challenge the traditional token-centric forecasting methodology prevalent in LLMs. By utilizing pre-encoded contextualized phrase vectors, CoG presents a shift towards optimizing computation efficiency while maintaining coherence and contextual relevance in generated text. This method posits a scalable solution to domain adaptation, steering the discourse of machine learning towards leveraging external corpora without modifying core training schemas.

From a practical standpoint, the implications are multifold. CoG's capacity to integrate and utilize expansive corpora efficiently aligns with the growing need for domain-specific customization in applications. Moreover, the research emphasizes ethical considerations inherent in copying phrases from existing documents, suggesting potential avenues for maintaining intellectual property rights integrity in future iterations of AI-driven text generation.

Future Outlook

The success of CoG prompts several future research avenues. Firstly, exploration into optimizing the retrieval stage and refining the contextual phrase representations could further enhance its performance. Secondly, the integration of CoG into other conditional text generation tasks, such as machine translation and summarization, remains an uncharted territory that could offer substantial advancements in those fields. Finally, addressing the ethical dimensions associated with text copyright and establishing clearer guidelines will be vital as such models continue to evolve in capability and applicability.

In conclusion, the paper "Copy is All You Need" offers a significant contribution to the field of text generation, presenting a salient alternative to traditional next-token prediction models. The concept of leveraging a substantial corpus for efficient and adaptive text generation not only amplifies the capabilities of LLMs but also sets a pivotal precedent for future developments in AI.